Parallelized and distributed bayesian regression analysis

Abstract

Systems, methods, and computer program products for estimating a Bayesian hierarchical regression model using parallelized and distributed Gibbs sampling are described. The techniques can be implemented to solve use cases where there is a response variable, e.g., number of store visits or web page visits, which is a variable of interest, and multiple explanatory variables, e.g., locations, temperatures, or prices, that may predict the response variable. The disclosed techniques build a model that explains and quantifies effects of the explanatory variables on the response variable on a distributed system. For instance, the disclosed techniques can build a model which has the capability to estimate that an X-degree increase in temperature at a certain time of year predicts a Y-percent increase in store visits. This estimation process is performed in parallel on multiple nodes of the distributed system.